Coordination of Pricing and Inventory Decisions: A Survey and Classification

Chan, Lap Mui Ann; Shen, Zuo-Jun; Simchi‐Levi, David; Swann, Julie

doi:10.1007/978-1-4020-7953-5_9

Cited by 216 publications

(124 citation statements)

References 130 publications

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“…Huh and Janakiraman (2005) propose an alternative approach for the optimality proof, which is based on the method used in Veinott (1966). For a review of other work in the pricing and inventory literature, the reader is referred to Petruzzi and Dada (1999), Elmaghraby and Keskinocak (2003) and Chan et al (2004).…”

Section: Introductionmentioning

confidence: 99%

Joint pricing and inventory control with a Markovian demand model

Yin

Rajaram

2007

European Journal of Operational Research

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We consider the joint pricing and inventory control problem for a single product with a finite horizon and periodic review. The demand distribution in each period is determined by an exogenous Markov chain. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. The surplus costs as well as fixed and variable costs are state dependent. We show the existence of an optimal (s, S, p)-type feedback policy for the additive demand model. We extend the model to the case of emergency orders and also incorporate capacity and service level constraints. We compute the optimal policy for a class of Markovian demand and illustrate the benefits of dynamic pricing over fixed pricing strategies through numerical examples. The results indicate that it is more beneficial to implement the dynamic pricing strategy in a Markovian demand environment with a high fixed ordering cost or with high demand uncertainty.

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Section: Introductionmentioning

confidence: 99%

Joint pricing and inventory control with a Markovian demand model

Yin

Rajaram

2007

European Journal of Operational Research

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“…Most researchers identify this as an area of future research. Developing strategies that integrate marketing and production decisions for profit maximization may lead to radical improvements in supply chain efficiencies for manufacturing firms (Chan et al, 2004).…”

Section: Literature Reviewmentioning

confidence: 99%

Coordination of pricing, advertising, and production decisions for multiple products

Bajwa

Sox

2015

IJSOM

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This research aims to develop and propose mathematical models that can be used to facilitate cross-functional coordination between operations and marketing. We consider a dynamic problem of joint pricing, advertising, and production decisions for a profit maximizing firm that produces multiple products. We assume the firm operates in monopolistic environment, where demand for its products is a function of price and advertising expenditure.We model the problem as a mixed integer nonlinear program, incorporating capacity constraints, setup costs, and demand seasonality. We first model and solve the pricing problem without advertising. Later, we extend the model to include advertising as decision variables. The demand for each product is assumed to be continuous, differentiable, strictly decreasing in price and concave in advertising. We present a solution approach which can be used for constant pricing, as well as dynamic pricing strategies. Furthermore, the solution approach is more general and applicable for linear as well as nonlinear demand functions.Using real world data from a manufacturer, we create problem instances, for different demand scenarios at different capacities, and solve for optimal prices for each strategy.We present analytical results that provide managerial insights on how the optimal prices change for different production plans and at different capacities. We compare the firm's profitability for the two pricing strategies, and show that dynamic pricing is valuable at low capacities and when at least one of the products has peak demand in the beginning of the planning horizon.We show that the optimal allocation of advertising budget across products does not change with budget changes. Moreover, the change is minimal with changes in demand seasonality. It is optimal to increase advertising in periods of higher demand and decrease in periods of lower demand. Hence, firms can use rules of thumb and need not to frequently review the allocation. Numerical results show that the proposed algorithms ii have good convergence properties.Finally, as it is clear from review of academic literature; there are no decision support systems that truly integrate the production/inventory and pricing decisions -specially for multi-product problems. We believe, this work makes valuable contributions in developing solution methodologies that can be incorporated in such decision support systems.iii DEDICATION I dedicate this dissertation to; my mother -who always made my education her first priority, my wife Ayesha -for her unconditional love, support, and care, and my children Raiha, Raza, and Rafay for providing an endless supply of hugs and kisses, and for making every day of my life so delightful.iv

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“…Our modeling choice favors instead the simplest framework that allows us to explore the interplay of coordination and uncertainty about (pricesensitive) demand in a revenue management context. Finally, our work is also related to a vast operations literature on coordinating pricing and inventory decisions, as reviewed by Chan et al (2004) and Fleischmann et al (2004). An important distinction is that models in this stream focus on storable goods rather than services.…”

Section: Relation To the Literaturementioning

confidence: 99%

Pricing and Revenue Management: The Value of Coordination

2014

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T he integration of systems for pricing and revenue management must trade off potential revenue gains against significant practical and technical challenges. This dilemma motivates us to investigate the value of coordinating decisions on prices and capacity allocation in a stylized setting. We propose two pairs of sequential policies for making static decisions-on pricing and revenue management-that differ in their degree of integration (hierarchical versus coordinated) and their pricing inputs (deterministic versus stochastic). For a large class of stochastic, price-dependent demand models, we prove that these four heuristics admit tractable solutions satisfying intuitive sensitivity properties. We further evaluate numerically the performance of these policies relative to a fully coordinated model, which is generally intractable. We find it interesting that near-optimal performance is usually achieved by a simple hierarchical policy that sets prices first, based on a nonnested stochastic model, and then uses these prices to optimize nested capacity allocation. This tractable policy largely outperforms its counterpart based on a deterministic pricing model. Jointly optimizing price and allocation decisions for the high-end segment improves performance, but the largest revenue benefits stem from adjusting prices to account for demand risk.

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Coordination of Pricing and Inventory Decisions: A Survey and Classification

Cited by 216 publications

References 130 publications

Joint pricing and inventory control with a Markovian demand model

Joint pricing and inventory control with a Markovian demand model

Coordination of pricing, advertising, and production decisions for multiple products

Pricing and Revenue Management: The Value of Coordination

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